During clinical diagnosis, the internal anatomy of a patient is imaged to determine how a certain disease has progressed. For example, the images may be used to help distinguish between infected tissues and healthy tissues within the patient. The images are also useful for radiotherapy treatment or planning or for surgical planning. Several modalities are used to generate images or functionality of anatomy of a patient which are suitable for diagnostic purposes, radiotherapy treatment or surgical planning. Examples include conventional X-ray imaging, computed tomography (CT) imaging, magnetic resonance imaging (MRI), and nuclear medicine imaging techniques, such as positron emission tomography (PET) and single photon emission computer tomography (SPECT).
In the case of radiation treatment (RT) planning, CT imaging is generally used because image voxel gray values (measured in Hounsfield Units) can be used directly in the calculation of radiation dosage. Clinicians, such as radiologists, dosimetrists or radiotherapists, typically must trace the outline of a few critical structures on a large number of images for RT planning. Manually drawing the individual contours on a contiguous set of 2D slices and combining them to form 3D volumes is very time consuming and labor intensive. The time and labor increases significantly with the number of image slices in the image set, as well as the number and size of the organs, tumors, etc. in the anatomical area of interest. The quality of the contouring and the resulting 3D objects depend on the resolution and contrast of the 2D images, and on the knowledge and judgment of the clinician performing the reconstruction. Accordingly, automated segmentation methods have been developed to address several of the problems with manual segmentation.
Typically, simulated CT images and digitally reconstructed radiographs (DRRS) are created for use in radiation therapy planning. The segmentation of bones is a critical task for the creation of a simulated CT and for the overall treatment plan. The bones have the largest electron density value in the body, which is in direct relation with the attenuation of ˜100 keV X-rays used in CT scanners, as well as the MeV rays used in radiation therapy. Accordingly, the accurate segmentation of bones is crucial to the entire process. However, if MR images are used, it is a difficult task to separate bone structures from other tissues on MR images. Many existing segmentation techniques for extracting bones on MR images, however, have disadvantages such as a risk of falsely classifying tissue and organs as bone and are not able to be automated.
It would be desirable to provide a system and method for automatically segmenting bones on MR images that provides improved performance including reliability and precision.